Gender: Male 
Age: 25-34
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Single 
First language: English 
Religion: Nothing 
Education: High school or below
Mother's education: High school or below
Father's education: High school or below
Self-assessed socioeconomic status (1-10): 3
Home: Rent 
Employment: Self-employed full time
Occupation: Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Mother's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Father's occupation: Prefer not to say
Hobbies: Listen to rock/indie music;Listen to hiphop and rap;Exercise;Use social media;Watch sports 
Devices used daily: Laptop;Smartphone 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language) 
NLP tools they would find useful: E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
Frequency of LLM use: Nearly every day
LLMs used: ChatGPT;Google Gemini;Meta Llama 2;Perplexity;Poe 
LLM use cases: Coding;Translation;Brainstorming;Writing (e.g., composing emails);Paraphrasing or finding synonyms;Proofreading/Editing (e.g., grammar, spelling, structure improvements);Solving math or logical problems;Analyze data;Learning (e.g., explaining complex concepts in simple terms) 
Contexts of LLM use: Work;School/University;Entertainment;Personal;Learning;Creative or artistic;Technical 
Prompt 1: I need help with a project ?
Prompt 2: Colab how to run a .py file?
Prompt 3: Give me a heart healthy meal plan to diet to follow for cutting and muscle gain.
Prompt 4: How much fish is safe to eat?
Prompt 5: Healthiest frozen chicken nuggets?
Prompt 6: I have a video In my google drive. I want to use colab to cut the video into 1 minute parts.
Prompt 7: What are good boy names that end with the letter N?
Prompt 8: How do I convert a huggingface page to colab?
Prompt 9: Energizer Lithium Ion AA vs Energizer max
Prompt 10: What is the cheapest premade meal company?
Comments: Thank you for the survey!